Get out of the black box of vanity churn metrics and gain the clarity you need to actually fix passive churn.
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See beneath the surface and gain a deep understanding of customer segments and churn drivers.
Low-hanging fruit becomes easy to spot when you know where to look.
With the highest quality data visualization, you can focus on improving the highest impact areas of your recovery process.
Without the right data format, results take too long, and low-quality analysis leads to misleading results.
All analysis starts with a daily rollup of failed payments. This enables you to accurately exclude transition periods, in progress campaigns, and outlier days.
Analyzing retention flows by comparing two random time periods is like flipping a coin with your recurring revenue. The key is to look at rolling time periods to surface natural variance and attribute results correctly.
Calculate recovery rate the right way, avoiding common mistakes that inflate recovery rate unrealistically.
Once a payment fails, there are only four possible outcomes. Some passive churn calculations will actually ignore active cancellations that end a recovery process prematurely, inflating recovery rate and hiding a key indicator.
Don't just look at recovery rate, zoom into recovery rate via retries alone, and via card updates alone. Also monitor cancellation rates, and passive churn rates. Trying “Smart” retries? Zoom into retry success for attribution.
Without identifying natural variance in your passive churn, you're bound to attribute natural ups and downs to the optimizations you're making. It's misleading, and makes fine-tuned optimization impossible.
Every company has natural variance in both their passive and active churn. Metrics go up and down day by day based on many external factors. We identify natural variance, which helps avoid attributing an increase or decrease to a strategy change that was simply going to happen anyway.
In cases where natural variance is high, segmentation can be used to zoom into segments with less variance - like customers that didn't use a discount, or customers who've placed several orders. You can also segment by processor, decline code, and just about any other attribute.
See the performance of all rolling time periods as a distribution, highlighting outliers, and if you've broken out of your typical performance range for any given metric.
A chronological view of rolling time periods, highlighting natural variance and adding context to the Swarm view. Key for performance comparisons.
Align all your failed payments on a single timeline, and examine the first 24 hours, next 24 hours, and so on. This is a powerful way to spot improvement areas in your payment recovery efforts.
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